{"id":160621,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/front-end-back-end-and-hybrid-techniques-to-noise-robust-speech-recognition\/"},"modified":"2018-10-16T21:44:03","modified_gmt":"2018-10-17T04:44:03","slug":"front-end-back-end-and-hybrid-techniques-to-noise-robust-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/front-end-back-end-and-hybrid-techniques-to-noise-robust-speech-recognition\/","title":{"rendered":"Front-End, Back-End, and Hybrid Techniques to Noise-Robust Speech Recognition"},"content":{"rendered":"<p>Noise robustness has long been an active area of research that captures significant interest from speech recognition researchers and developers. In this chapter, with a focus on the problem of uncertainty handling in robust speech recognition, we use the Bayesian framework as a common thread for connecting, analyzing, and categorizing a number of popular approaches to the solutions pursued in the recent past. The topics covered in this chapter include 1) Bayesian decision rules with unreliable features and unreliable model parameters; 2) principled ways of computing feature uncertainty using structured speech distortion models; 3) use of a phase factor in an advanced speech distortion model for feature compensation; 4) a novel perspective on model compensation as a special implementation of the general Bayesian predictive classification rule capitalizing on model parameter uncertainty; 5) taxonomy of noise compensation techniques using two distinct axes, feature vs. model domain and structured vs. unstructured transformation; and 6) noise-adaptive training as a hybrid feature-model compensation framework and its various forms of extension.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Noise robustness has long been an active area of research that captures significant interest from speech recognition researchers and developers. In this chapter, with a focus on the problem of uncertainty handling in robust speech recognition, we use the Bayesian framework as a common thread for connecting, analyzing, and categorizing a number of popular approaches [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"deng"}],"msr_publishername":"Springer Verlag","msr_publisher_other":"","msr_booktitle":"D. Kolossa and R. Hab-Umbach (eds.) Robust Speech Recognition of Uncertain Data","msr_chapter":"4","msr_edition":"D. Kolossa and R. Hab-Umbach (eds.) 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